ESR2: Marco Andres Acevedo Zamora

Marco A. Acevedo Zamora

ESR2

Project Overview

This work will use Artificial Intelligence tools for polymetallic ore and gangue characterization that are promising for revolutionizing microscopic studies of thin/thick sections (Petrography), a common ground between Industry and Academia, benefiting geological studies and the society.

We want to develop new data acquisition (1^st work package, WP1) and analysis tool software (2^nd work package, WP2) to elaborate empirical models for predicting trace element behavior. The WP1 is scheduled for the end the project and include the development of Ultra-fast LA-ICP-MS analysis. The WP2 include developing software to process microscopy image Big Data from state-of-the-art analytical techniques, using Image Processing and combining their pixel information at an adequate resolution for representation and spectral study using a correlative microscopy approach.

By characterizing and modeling trace elements in a pilot software platform, we are looking forward to for increasing resource efficiency in the mining value chain. The methodology development objectives are be divided in two work packages.

Publications

https://scholar.google.com/citations?user=eUXJEfoAAAAJ&hl=en

https://www.researchgate.net/profile/Marco_Acevedo2

Acevedo, M. (2016). Emplacement and Magmatic Evolution of the Val Fredda Complex intrusions (southern Adamello Batholith, N. Italy). MSc in Geology thesis archive, 177 p., University of Geneva (UNIGE). https://archive-ouverte.unige.ch/pages/masters?all_subtypes=0

Conference Presentations

Acevedo, M. (2020). “Novel ways of automated trace element-mineral association recognition.” Manuscript accepted in Conference in Minerals Engineering, February 2020. Lulea, Sweden.

L.S. Jordán, E. Sánchez, M. Acevedo, M.C. Lázaro (2014). Controles estructurales en el distrito minero de Tintaya. XVII Congreso Peruano de Geología. Resúmenes extendidos, Boletín 109-14 (Sociedad Geológica del Perú). http://www.sgp.org.pe/category/bibliovirtual/?result=7210

Courses

Deep Learning specialization. Andrew Ng. at Coursera online (Aug 2020).